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Research Papers: Gas Turbines: Cycle Innovations

Multidimensional Load Estimation Algorithms That Enable Performance Analysis of Industrial Gas Turbines Without A Priori Information

[+] Author and Article Information
Takuya Yoshida

Power and Industrial Systems R&D Laboratory, Hitachi Ltd., 3-1-1 Saiwaicho, Hitachi, Ibaraki 317-8511, Japantakuya.yoshida.ru@hitachi.com

Masaaki Bannai

Urban Planning and Development Systems, Hitachi Ltd., 14-1 Sotokanda 4-chome, Chiyoda-ku, Tokyo 101-8010, Japanmasaaki.bannai.xu@hitachi.com

Minoru Yoshida

Energy Solution Engineering Division, Hitachi Engineering & Services Co., Ltd., 15-1 Higashiohnumacho 1-chome, Hitachi, Ibaraki 316-0023, Japanminoru.yoshida.gs@hitachi-hes.com

Hiroyuki Yamada

Energy Solution Engineering Division, Hitachi Engineering & Services Co., Ltd., 15-1 Higashiohnumacho 1-chome, Hitachi, Ibaraki 316-0023, Japanhiroyuki.yamada.ah@hitachi-hes.com

Masaki Ishikawa

Energy Solution Engineering Division, Hitachi Engineering & Services Co., Ltd., 15-1 Higashiohnumacho 1-chome, Hitachi, Ibaraki 316-0023, Japanmasaki.ishikawa.vw@hitachi-hes.com

J. Eng. Gas Turbines Power 130(4), 041703 (Apr 28, 2008) (10 pages) doi:10.1115/1.2900727 History: Received March 21, 2007; Revised January 16, 2008; Published April 28, 2008

Performance analysis and diagnosis for gas turbines usually assume the use of detailed design specifications or similar kinds of information for building and configuring engine models. This allows the nonlinearity of gas turbine performance characteristics to be taken into account. However, this approach tends to make it difficult for users of industrial gas turbines to analyze performance because (1) detailed design specifications are not necessarily supplied to the users, and (2) even if they were available, use of these kinds of information may often lead to complex procedures for model building and for making adjustments and configurations that all require high expertise. The purpose of this paper was to propose a direct modeling approach based only on operating data and not requiring a priori information like manufacturer-supplied specifications while preserving sufficient accuracy. The core element of this approach was the automatic identification and selection of base load operating data from various operating conditions. A set of load estimation algorithms was proposed. They were applied to 31,000h of operating data for two types of engines, which involved actual failure occurrences, and subsequent performance modeling and analysis were carried out. The following results were obtained. (1) The relative performance trends obtained revealed the quantitative extent of degradation during operation and of recovery by repair or engine change. (2) The performance trends gave a good account of actual failures. (3) The accuracy of the performance modeling measured by the 99th percentile of error was on the order of 1%. The proposed direct modeling approach offers sufficient accuracy to quantify the gradual degradation of performance and its recovery by maintenance. The performance trends obtained are useful for further fault diagnosis.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

Procedure of base load data selection by output ratio algorithm

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Figure 2

Procedure of base load data selection by exhaust temperature algorithm

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Figure 3

Procedure of base load data selection by MT algorithm

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Figure 4

Relative performance evaluated for Engine A (base load data selection by MT algorithm)

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Figure 5

Comparison of error and selection ratio according to algorithms of base load data selection (Engine A)

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Figure 6

Identified base load data range according to algorithms of selection (Engine A)

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Figure 7

Trade-off between error and selection ratio according to algorithms of base load data selection

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Figure 8

Sensitivity of selection ratio and error to threshold

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Figure 9

Relative performance evaluated for Engine B (base load selection by MT algorithm)

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Figure 10

Accuracy and selection ratio for the engines studied

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Figure 11

Relative performance evaluated for Engine B according to algorithms of base load data selection

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Figure 12

Identified base load data range according to algorithms of selection (Engine B)

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